AutoGen MCP for AI. Orchestrate entire agent teams and debug their conversations.
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…and any MCP-compatible client
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AutoGen MCP manages complex, multi-agent AI workflows. Define customized LLM roles, run isolated sessions, map out entire agent topologies, and get deep traces of every conversation between specialized agents from any AI client.
What your AI can do
Create agent
Defines a new customized agent with specific parameters and roles.
Create message
Sends an initial or follow-up message to start or continue a running agent session.
List skills
Lists available Python functions that can be used by the agents to perform external tasks.
Create specialized AI agents with custom parameters and roles (like user proxies or critics) for specific tasks.
Start a clean, blank memory space to run a multi-agent workflow without mixing it up with previous tasks.
Retrieve deep message history, showing every back-and-forth conversation between agents inside the system's logging structure.
Map out and view the entire graph of agent dependencies and available pre-defined workflow topologies.
Review existing constrained fallback LLM settings that the system uses for running agents.
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AutoGen MCP: 10 Tools for Agent Workflow Management
These tools let you control every aspect of agent behavior, including defining new agents, managing sessions, listing available skills, and tracing message history.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using AutoGen on VinkiusCreate Agent
Defines a new customized agent with specific parameters and roles.
Create Message
Sends an initial or follow-up message to start or continue a running agent session.
List Skills
Lists available Python functions that can be used by the agents to perform external...
Create Session
Sets up a new, isolated memory space for multi-agent workflows.
Delete Session
Permanently removes an existing agent conversation session from the system.
List Agents
Retrieves a list of all customized agents currently configured in the instance.
List Messages
Fetches the complete message history for any specific agent session.
List Models
Lists all constrained Large Language Models available for use in the agents.
List Sessions
Retrieves a list of all active or completed agent conversation sessions.
List Workflows
Retrieves a list of all pre-defined, multi-agent workflow topologies.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The AutoGen integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AutoGen, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Microsoft AutoGen. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Sandboxed per request
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Tracking multi-step automation is a constant headache.
Today, running an automated process feels like managing five different dashboards. You kick off the job in one tool, then copy the output to the next system, wait for it to complete, and finally paste its results into a third place just to check if everything worked. If anything breaks—which it always does—you have to manually jump between logs, tabs, and interfaces until you find the exact point of failure.
With this MCP, you define the entire process once. You tell your AI client to initiate the workflow, and it manages the whole chain internally. You get a single source of truth for the execution log, showing exactly which agent failed, why it failed, and what step needs adjusting.
Viewing Agent Conversation Traces with `list_messages`
Before this MCP, finding out *why* an agent made a decision required digging through raw, unstructured server logs. You'd be sifting through timestamps and generic error messages, trying to piece together who said what and in what order.
Now, `list_messages` gives you structured history. It presents the deep, conversational traces between agents—showing the User Proxy’s input, followed by the Coder Agent’s output, and then the Critic Agent's rejection with specific reasons. You see the full thought process.
What your AI can actually do with this
This connector lets you build and monitor advanced AI teams. Instead of writing one prompt for a single task, you define several cooperating agents—like a coder, a reviewer, and a project manager—that talk to each other until the job is done. You can create completely isolated memory spaces for these workflows so they run cleanly without interference.
Furthermore, you can visualize how the entire process flows, mapping out all the steps an agent takes from start to finish. If your automated tasks get complicated, this MCP gives you full visibility into the execution logs and conversations between agents, letting you debug exactly where things went wrong in a complex chain of actions.
Connecting via Vinkius means you can access this whole suite of capabilities directly from Claude, Cursor, or any other AI client.
019d7556-0343-72c6-b82c-4af43758d443 
Here's how it actually works
The bottom line is that you control an entire team of specialist AIs from one place.
First, subscribe to this MCP and provide your AutoGen Studio instance Base URL.
Next, dispatch a request from your AI client—this tells the system which workflow or agent group needs to start.
Finally, you get back detailed logs showing every step, message, and decision made by the entire swarm of specialized agents.
Who is this actually for?
This MCP is built for technical people who run automated systems. If your job involves complex backend processes or coordinating multiple specialized AI tools, you need this. It's for the engineer tired of manually checking logs across five different services.
Uses the MCP to audit running agent topologies and trace deep execution logs to quickly iterate on Python skills without having to switch contexts.
Verifies the health and step-by-step progress of automated backend swarm operations before they go live for users.
Extracts deep LLM-to-LLM conversational histories across experimental boundaries, which is crucial for grading complex AI models.
What Changes When You Connect
You can define new roles for your agents using the create_agent tool. This means you're not just running a generic AI; you're setting up specialized workers like 'Coder' or 'Critic'.
Need to start fresh? Use create_session to build an isolated memory space for multi-agent workflows. Nothing from the previous run can pollute this one.
The most useful thing is debugging: tools like list_messages let you pull deep agent-to-agent traces, showing exactly why a decision was made or where the conversation stalled.
Don't get lost in complexity; use list_workflows to map out all predefined topologies and know what kind of automated process you can run next.
Keep track of everything with list_sessions, giving you a clear overview of every agent group that has been active or completed in the system.
See it in action
Debugging a failed code review loop
A development team's automated process fails because the Coder and Critic agents disagree on an API key format. Instead of guessing, you use list_messages to pull the full conversation trace, pinpointing that the failure stemmed from a hard-coded key issue in the original script.
Running complex market research
A Product Manager needs to run an experimental 'Market Research' workflow. They use create_session first, then execute the workflow, allowing the agent group to gather and synthesize data before they even look at a single line of output.
Validating backend automation steps
An engineer needs to verify that a new payment processing system will handle edge cases. They use list_skills to check the available Python functions and then define specialized agents via create_agent to simulate the required user roles.
Analyzing multiple experiments
A researcher runs five different LLM-to-LLM dialogue simulations. By using list_sessions, they can quickly retrieve and grade the complete message history from each one without mixing up data or having to restart the process.
The honest tradeoffs
Treating agents as single prompts
Asking your AI client to run a complex workflow by just pasting in a long paragraph of text. This forces the agent to try and do everything at once, usually failing midway.
You must use the dedicated tools. Start by using list_workflows to see what's possible, then use create_session before sending your initial prompt via create_message. Never rely on a single text input.
Forgetting to clean up sessions
Running multiple tests over days and letting the system fill with old data. The agent gets confused by irrelevant history, slowing down every subsequent run.
Always call delete_session when a test or project is done. This keeps your memory space clean and ensures new runs start with fresh context.
Assuming the best model works
Starting an agent process without checking which LLMs are available, leading to vague errors about unsupported models.
Before starting anything, run list_models to see exactly what constrained fallback OpenAI configurations are loaded into your specific instance. This prevents runtime surprises.
When It Fits, When It Doesn't
Use this MCP if your task involves more than one specialized AI component working together in a defined sequence. If you're trying to automate complex tasks like code generation, data validation, or multi-step research, this is the right place. Don't use it if your goal is simply 'write me an email.' For simple tasks, just send a direct message. You need to manage state and collaboration; therefore, start by checking list_workflows to see if a pre-built topology already exists for your problem.
Questions you might have
Can my AI agent debug a looping multi-agent conversation? +
Yes. You can instruct your primary agent to retrieve the message traces for a specific AutoGen session ID. It will instantly unpack the internal LLM-to-LLM conversation, highlighting exactly which secondary agent is looping, throwing errors, or deviating from the constraints without manual log parsing.
How do I add a new Python capability or skill dynamicly? +
Your agent can list currently mapped Python skills bound to the studio runtime. If you need a new capability, your primary AI can iterate on the script directly on your CLI/editor and once deployed in your studio, you can map it natively to customized agents via the creation parameters.
Can it trigger a Workflow to start executing a new complex task? +
Absolutely. Ask your agent to create a fresh, blank, and completely isolated session, then dispatch a newly constructed 'human message' targeting an existing Multi-Agent workflow topology. It initiates the whole automated logic sequence securely and remotely.
How do I check the full history of an agent conversation using `list_messages`? +
Yes, it retrieves the complete message trace. You provide a session ID and get every human prompt and every agent-to-agent reply that happened up to this point. This is crucial for debugging why agents made certain decisions.
When running complex tasks, how do I manage memory boundaries using `create_session` or `delete_session`? +
They are completely isolated from each other. When you call create_session, you get a fresh, blank context (a new UUID). Once the task is done, use delete_session to permanently wipe that entire history, preventing data bleed between runs.
How do I see which customized agents are available in my environment using `list_agents`? +
This command provides a manifest of all your defined agent roles. Running list_agents shows every specialized entity—like the Coder or Critic—that's ready to participate in a workflow without needing manual setup.
Are my LLM configurations compatible? How do I audit them using `list_models`? +
You can easily check your current LLM options. Calling list_models audits the constrained fallback OpenAI configurations stored in the instance. This confirms exactly which underlying models are attached and ready for use across all workflows.
Where do I find out what external tools my agents can access? What does `list_skills` show? +
It lists every Python skill function you've injected into the system. This tells your AI client exactly what external capabilities, like database lookups or API calls, are available for your agents to use when they execute a task.
We've already built the connector for AutoGen. Just plug in your AI agents and start using Vinkius.
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